Project: Investigate a Dataset - TMDb Movies

Table of Contents

• Introduction
• Data Wrangling
• Exploratory Data Analysis
• Conclusions

Introduction

Dataset Description

About Dataset: This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue.

The columns include:
id: The unique identifier for each movie.
imdb_id: The unique identifier for each movie on IMDB site.
popularity: Popularity for each movie.
budget: Movie budget
revenue: Movie revenue
original_title: Movie title
cast: Movie cast
homepage: Movie webpage link
director: Movie director
tagline: Movie tagline
keywords: Movie keywords
overview: Movie overview
runtime: Movie runtimes
genres: Movie genres
production_companies: Movie production
release_date: Date of movive release
vote_count: Movie vote count
vote_average: Movie vote average
budget_adj: Movie budget of the associated movie in terms of 2010 dollars, accounting for inflation over time.
revenue_adj: Movie revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time.

Question(s) for Analysis

1) Top directors with most movies
2) Top actors with most movies
3) Top actors and directors with high mean ratings
4) Top actors and directors with popularity ratings
5) Year with most movies released
6) Trendline of popularity and value average
7) Which genres are most popular from year to year?
8) What kind of properties are asssociated with movies of high revenues?

# Use this cell to set up import statements for all of the packages that you plan to use.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Data Wrangling

# Load your data and print out a few lines.

df = pd.read_csv('tmdb-movies.csv')

df.head(2)

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	Twenty-two years after the events of Jurassic ...	124	Action|Adventure|Science Fiction|Thriller	Universal Studios|Amblin Entertainment|Legenda...	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	An apocalyptic story set in the furthest reach...	120	Action|Adventure|Science Fiction|Thriller	Village Roadshow Pictures|Kennedy Miller Produ...	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08

2 rows × 21 columns

Assessing the data

# Check out the number of rows and columns in the dataset 

df.shape

(10866, 21)

# Assess the summary of each column in the dataset; column name, number of items and type of data

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10866 entries, 0 to 10865
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    10866 non-null  int64  
 1   imdb_id               10856 non-null  object 
 2   popularity            10866 non-null  float64
 3   budget                10866 non-null  int64  
 4   revenue               10866 non-null  int64  
 5   original_title        10866 non-null  object 
 6   cast                  10790 non-null  object 
 7   homepage              2936 non-null   object 
 8   director              10822 non-null  object 
 9   tagline               8042 non-null   object 
 10  keywords              9373 non-null   object 
 11  overview              10862 non-null  object 
 12  runtime               10866 non-null  int64  
 13  genres                10843 non-null  object 
 14  production_companies  9836 non-null   object 
 15  release_date          10866 non-null  object 
 16  vote_count            10866 non-null  int64  
 17  vote_average          10866 non-null  float64
 18  release_year          10866 non-null  int64  
 19  budget_adj            10866 non-null  float64
 20  revenue_adj           10866 non-null  float64
dtypes: float64(4), int64(6), object(11)
memory usage: 1.7+ MB

# Check the statistical summary of the numerical columns in the dataset.

df.describe()

	id	popularity	budget	revenue	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj
count	10866.000000	10866.000000	1.086600e+04	1.086600e+04	10866.000000	10866.000000	10866.000000	10866.000000	1.086600e+04	1.086600e+04
mean	66064.177434	0.646441	1.462570e+07	3.982332e+07	102.070863	217.389748	5.974922	2001.322658	1.755104e+07	5.136436e+07
std	92130.136561	1.000185	3.091321e+07	1.170035e+08	31.381405	575.619058	0.935142	12.812941	3.430616e+07	1.446325e+08
min	5.000000	0.000065	0.000000e+00	0.000000e+00	0.000000	10.000000	1.500000	1960.000000	0.000000e+00	0.000000e+00
25%	10596.250000	0.207583	0.000000e+00	0.000000e+00	90.000000	17.000000	5.400000	1995.000000	0.000000e+00	0.000000e+00
50%	20669.000000	0.383856	0.000000e+00	0.000000e+00	99.000000	38.000000	6.000000	2006.000000	0.000000e+00	0.000000e+00
75%	75610.000000	0.713817	1.500000e+07	2.400000e+07	111.000000	145.750000	6.600000	2011.000000	2.085325e+07	3.369710e+07
max	417859.000000	32.985763	4.250000e+08	2.781506e+09	900.000000	9767.000000	9.200000	2015.000000	4.250000e+08	2.827124e+09

Data Cleaning

From assessing the data, we can see that some columns have null values.
In this section, we will check for duplicates and take them out, then we find a solution to the null value instances.
Some unnecessary columns would be taken out, while some derived columns will be created.
Some datatypes are not appropriate and they will be changed.

# Check for duplicates

df.duplicated().sum()

1

# Take out duplicate rows and re-check

df.drop_duplicates(inplace=True)

df.duplicated().sum()

0

Creating derived columns and dropping unneeded columns

New columns like profit, actor, genre and production_company will be derived from existing columns.

Columns like id, imdb_id, budget, revenue, homepage, tagline, keywords, overview, genres and
production_companies will be dropped.

# Create `profit` column
# The Profit column was gotten by getting the difference between revenue and budget 

df['profit'] = df.revenue_adj - df.budget_adj

df.head(2)

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	124	Action|Adventure|Science Fiction|Thriller	Universal Studios|Amblin Entertainment|Legenda...	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09	1.254446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	120	Action|Adventure|Science Fiction|Thriller	Village Roadshow Pictures|Kennedy Miller Produ...	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08	2.101614e+08

2 rows × 22 columns

# The actor, genre and production_company column are gotten by getting the names before the first '|' character in the cast column.

df['actor']=df.cast.str.split('|', n=1).str.get(0)
df['genre']=df.genres.str.split('|', n=1).str.get(0)
df['production_company']=df['production_companies'].str.split('|', n=1).str.get(0)

df.head(2)

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	actor	genre	production_company
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09	1.254446e+09	Chris Pratt	Action	Universal Studios
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08	2.101614e+08	Tom Hardy	Action	Village Roadshow Pictures

2 rows × 25 columns

# Dropping all unnecessary columns

df.drop(['id', 'imdb_id', 'budget', 'revenue', 'cast', 'homepage', 'tagline', 'keywords', 'overview', 'genres', 'production_companies'], axis=1, inplace=True)

df.head(2)

	popularity	original_title	director	runtime	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	actor	genre	production_company
0	32.985763	Jurassic World	Colin Trevorrow	124	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09	1.254446e+09	Chris Pratt	Action	Universal Studios
1	28.419936	Mad Max: Fury Road	George Miller	120	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08	2.101614e+08	Tom Hardy	Action	Village Roadshow Pictures

# Taking another look at the columns with null_values

df.isnull().sum()

popularity               0
original_title           0
director                44
runtime                  0
release_date             0
vote_count               0
vote_average             0
release_year             0
budget_adj               0
revenue_adj              0
profit                   0
actor                   76
genre                   23
production_company    1030
dtype: int64

Dealing with null values

It is observed that all numerical columns are void of null values, while the categorical columns which are strings are the ones with null values.
So, the null spaces will be filled with the string 'unspecified'.

# Fill the null_values with 'unspecified' and check for null values again.

df[['director','actor','production_company','genre']]=df[['director','actor','production_company','genre']].fillna('unspecified')

df.isnull().sum()

popularity            0
original_title        0
director              0
runtime               0
release_date          0
vote_count            0
vote_average          0
release_year          0
budget_adj            0
revenue_adj           0
profit                0
actor                 0
genre                 0
production_company    0
dtype: int64

Data Types

The data types of each column will be rechecked to ensure the right format of data for all columns.
Anyone found not appropriate will be converted to the correct one.

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10865 entries, 0 to 10865
Data columns (total 14 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   popularity          10865 non-null  float64
 1   original_title      10865 non-null  object 
 2   director            10865 non-null  object 
 3   runtime             10865 non-null  int64  
 4   release_date        10865 non-null  object 
 5   vote_count          10865 non-null  int64  
 6   vote_average        10865 non-null  float64
 7   release_year        10865 non-null  int64  
 8   budget_adj          10865 non-null  float64
 9   revenue_adj         10865 non-null  float64
 10  profit              10865 non-null  float64
 11  actor               10865 non-null  object 
 12  genre               10865 non-null  object 
 13  production_company  10865 non-null  object 
dtypes: float64(5), int64(3), object(6)
memory usage: 1.2+ MB

type(df['release_date'][1])

str

# It is observed that `release_year` is a string instead of datetime format
# To change release_date to date-time and recheck

df.release_date = pd.to_datetime(df.release_date)

type(df.release_date[1])

pandas._libs.tslibs.timestamps.Timestamp

# Creating the derived column 'release_month' from `release_date'

df['release_month'] = df['release_date'].dt.month

df.head(3)

	popularity	original_title	director	runtime	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	actor	genre	production_company	release_month
0	32.985763	Jurassic World	Colin Trevorrow	124	2015-06-09	5562	6.5	2015	1.379999e+08	1.392446e+09	1.254446e+09	Chris Pratt	Action	Universal Studios	6
1	28.419936	Mad Max: Fury Road	George Miller	120	2015-05-13	6185	7.1	2015	1.379999e+08	3.481613e+08	2.101614e+08	Tom Hardy	Action	Village Roadshow Pictures	5
2	13.112507	Insurgent	Robert Schwentke	119	2015-03-18	2480	6.3	2015	1.012000e+08	2.716190e+08	1.704191e+08	Shailene Woodley	Adventure	Summit Entertainment	3

# Assessing the cleaned data

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10865 entries, 0 to 10865
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   popularity          10865 non-null  float64       
 1   original_title      10865 non-null  object        
 2   director            10865 non-null  object        
 3   runtime             10865 non-null  int64         
 4   release_date        10865 non-null  datetime64[ns]
 5   vote_count          10865 non-null  int64         
 6   vote_average        10865 non-null  float64       
 7   release_year        10865 non-null  int64         
 8   budget_adj          10865 non-null  float64       
 9   revenue_adj         10865 non-null  float64       
 10  profit              10865 non-null  float64       
 11  actor               10865 non-null  object        
 12  genre               10865 non-null  object        
 13  production_company  10865 non-null  object        
 14  release_month       10865 non-null  int64         
dtypes: datetime64[ns](1), float64(5), int64(4), object(5)
memory usage: 1.6+ MB

df.describe()

	popularity	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	release_month
count	10865.000000	10865.000000	10865.000000	10865.000000	10865.000000	1.086500e+04	1.086500e+04	1.086500e+04	10865.000000
mean	0.646446	102.071790	217.399632	5.975012	2001.321859	1.754989e+07	5.136900e+07	3.381911e+07	6.827612
std	1.000231	31.382701	575.644627	0.935138	12.813260	3.430753e+07	1.446383e+08	1.252194e+08	3.441764
min	0.000065	0.000000	10.000000	1.500000	1960.000000	0.000000e+00	0.000000e+00	-4.139124e+08	1.000000
25%	0.207575	90.000000	17.000000	5.400000	1995.000000	0.000000e+00	0.000000e+00	0.000000e+00	4.000000
50%	0.383831	99.000000	38.000000	6.000000	2006.000000	0.000000e+00	0.000000e+00	0.000000e+00	7.000000
75%	0.713857	111.000000	146.000000	6.600000	2011.000000	2.085325e+07	3.370173e+07	1.293091e+07	10.000000
max	32.985763	900.000000	9767.000000	9.200000	2015.000000	4.250000e+08	2.827124e+09	2.750137e+09	12.000000

Exploratory Data Analysis

# Which Director has more movies.

dir_count = df['director'].value_counts().head(10)

dir_count.plot(kind='bar', figsize=(8,5))
plt.title('Top 10 Directors with Most Movies', fontsize=16)
plt.xlabel('Directors', fontsize=14)
plt.ylabel('No of Movies ', fontsize=14)

Text(0, 0.5, 'No of Movies ')

# Which Actor has more movies

dir_count = df['actor'].value_counts().head(11)[1:]

dir_count.plot(kind='bar', figsize=(8,5))
plt.title('Top 10 Actors with Most Movies', fontsize=16)
plt.xlabel('Actors', fontsize=14)
plt.ylabel('No of Movies ', fontsize=14)

Text(0, 0.5, 'No of Movies ')

# Top directors with high mean ratings

dir_ratings=df.groupby(['director'], as_index=False)['vote_average'].mean().sort_values('vote_average', ascending=False).head(10)

dir_ratings.plot(x='director', y='vote_average', kind='bar', figsize=(8,5))
plt.title('Top 10 Directors with Most Rating', fontsize=16)
plt.xlabel('Directors', fontsize=14)
plt.ylabel('Average Ratings', fontsize=14)

Text(0, 0.5, 'Average Ratings')

# Genres with the highest average rating

genre_ratings=df.groupby(['genre'], as_index=False)['vote_average'].mean().sort_values('vote_average', ascending=False).head(10)

genre_ratings.plot(x='genre', y='vote_average', kind='bar', figsize=(8,5))
plt.title('Top 10 Genres with Most Rating', fontsize=16)
plt.xlabel('Genres', fontsize=14)
plt.ylabel('Average Ratings', fontsize=14)

Text(0, 0.5, 'Average Ratings')

# Top actors with high mean ratings

act_ratings=df.groupby(['actor'], as_index=False)['vote_average'].mean().sort_values('vote_average', ascending=False).head(10)

act_ratings.plot(x='actor', y='vote_average', kind='bar', figsize=(8,5))
plt.title('Top 10 Actors with Most Rating', fontsize=16)
plt.xlabel('Actors', fontsize=14)
plt.ylabel('Average Ratings', fontsize=14)

Text(0, 0.5, 'Average Ratings')

# Directors with more popular movies on an average

dir_pop=df.groupby(['director'], as_index=False)['popularity'].mean().sort_values('popularity', ascending=False).head(10)

dir_pop.plot(x='director', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Directors with Popular Movies', fontsize=16)
plt.xlabel('Directors', fontsize=14)
plt.ylabel('Average Popularity', fontsize=14)

Text(0, 0.5, 'Average Popularity')

# production_companies with more popular movies on an average

dir_pop=df.groupby(['production_company'], as_index=False)['popularity'].sum().sort_values('popularity', ascending=False).head(10)

dir_pop.plot(x='production_company', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Production_company with Popular Movies', fontsize=16)
plt.xlabel('production_company', fontsize=14)
plt.ylabel('Average Popularity', fontsize=14)

Text(0, 0.5, 'Average Popularity')

df['popularity'].mean()

0.6464455549010583

df['vote_average'].mean()

5.975011504832047

# Directors with popular movies on a cummulative basis

dir_pop_cum=df.groupby(['director'], as_index=False)['popularity'].sum().sort_values('popularity', ascending=False).head(10)

dir_pop_cum.plot(x='director', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Directors with Cumm. Popularity', fontsize=16)
plt.xlabel('Directors', fontsize=14)
plt.ylabel('Cumm Popularity', fontsize=14)

Text(0, 0.5, 'Cumm Popularity')

#Actors with more popular movies on an average

act_pop=df.groupby(['actor'], as_index=False)['popularity'].mean().sort_values('popularity', ascending=False).head(10)

act_pop.plot(x='actor', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Actors with Popular Movies', fontsize=16)
plt.xlabel('Actors', fontsize=14)
plt.ylabel('Average Popularity', fontsize=14)

Text(0, 0.5, 'Average Popularity')

# Actors with popular movies on a cummulative basis

act_pop_cum=df.groupby(['actor'], as_index=False)['popularity'].sum().sort_values('popularity', ascending=False).head(10)

act_pop_cum.plot(x='actor', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Actors with Cumm. Popularity', fontsize=16)
plt.xlabel('Actors', fontsize=14)
plt.ylabel('Cumm Popularity', fontsize=14)

Text(0, 0.5, 'Cumm Popularity')

#Genres with more popular movies on an average

genre_pop=df.groupby(['genre'], as_index=False)['popularity'].mean().sort_values('popularity', ascending=False).head(10)

genre_pop.plot(x='genre', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Genres with Popular Movies', fontsize=16)
plt.xlabel('Genres', fontsize=14)
plt.ylabel('Average Popularity', fontsize=14)

Text(0, 0.5, 'Average Popularity')

# Genres with popular movies on a cummulative basis

genre_pop_cum=df.groupby(['genre'], as_index=False)['popularity'].sum().sort_values('popularity', ascending=False).head(10)

genre_pop_cum.plot(x='genre', y='popularity', kind='bar', figsize=(8,5))
plt.title('Top 10 Genres with Cumm. Popularity', fontsize=16)
plt.xlabel('Genres', fontsize=14)
plt.ylabel('Cumm Popularity', fontsize=14)

Text(0, 0.5, 'Cumm Popularity')

# Quantity of movies produced each year

df['release_year'].value_counts().plot(kind='bar', figsize=(12,6))
plt.title('Years with Most Movies Released', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('No of Movies', fontsize=14)

Text(0, 0.5, 'No of Movies')

# Trendline of average movie ratings over the years

df.groupby('release_year').mean()['vote_average'].plot(kind='line', figsize=(15,7))
plt.title('Trend of Average Rating Along the Years', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Vote Average', fontsize=14)

Text(0, 0.5, 'Vote Average')

# Trendline of average movie popularity over the years

df.groupby('release_year').mean()['popularity'].plot(kind='line', figsize=(15,7))

plt.title('Trend of Average Popularity Along the Years', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Popularity', fontsize=14)

Text(0, 0.5, 'Popularity')

NOTE:

Take note that the data has some revenue_adj and budget_adj spaces containing 0 values.
Every case of this will result in an incorrect economical record of the movie, which in turn affects the general overview of the data.
To rectify this, we takeaway every of this case and see if what is left is enough to give an wholistic insights.

df_eco =  df[(df['budget_adj']!=0.0) & (df['revenue_adj']!=0.0)]

df_eco.shape

(3854, 15)

The dataset has been reduced by over 50%. It most likely is not ideal for insights, especially in contrast with the original dataset.
But, we'll go ahead to use it for some economic based insghts, even though we don't trust it's integrity.

# Most profitable Movies (profit)

top_profit = df_eco.sort_values('profit', ascending=False)[['original_title','profit']].head(10)

top_profit.plot(x='original_title', y='profit', kind='bar', figsize=(8,5))

plt.title('Top 10 Most Profitable Movies', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Profit', fontsize=14)

Text(0, 0.5, 'Profit')

# Most expensive Movies(budget_adj)

top_budget = df_eco.sort_values('budget_adj', ascending=False)[['original_title','budget_adj']].head(10)

top_budget.plot(x='original_title', y='budget_adj', kind='bar', figsize=(8,5))

plt.title('Top 10 Most Expensive Movies', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Budget', fontsize=14)

Text(0, 0.5, 'Budget')

# Highest Grossing Movies(revenue_adj)

top_gross = df_eco.sort_values('revenue_adj', ascending=False)[['original_title','revenue_adj']].head(10)

top_gross.plot(x='original_title', y='revenue_adj', kind='bar', figsize=(8,5))

plt.title('Top 10 Most Grossed Movies', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Revenue', fontsize=14)

Text(0, 0.5, 'Revenue')

# Trendline of cummulative profit over the years

df_eco.groupby('release_year').sum()['profit'].plot(kind='line', figsize=(15,7))

plt.title('Trend of Profit Along the Years', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Profit', fontsize=14)

Text(0, 0.5, 'Profit')

# Trendline of mean profit over the years

df_eco.groupby('release_year').mean()['profit'].plot(kind='line', figsize=(15,7))

plt.title('Trend of Ave. Profit Along the Years', fontsize=16)
plt.xlabel('Years', fontsize=14)
plt.ylabel('Profit', fontsize=14)

Text(0, 0.5, 'Profit')

Finding Relationships Between Variables

# Take a broad overview through pairplots

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x1e6f42e9370>

# This is a pairplot to consider a more accurate economic performance of movies.

sns.pairplot(df_eco)

<seaborn.axisgrid.PairGrid at 0x1e6fdc4afd0>

Which genres are most popular from year to year?

df[['release_year','genre', 'actor']].groupby(['release_year','genre']).count().head(40)

		actor
release_year	genre
1960	Action	8
	Adventure	2
	Comedy	7
	Crime	1
	Drama	5
	Horror	6
	Thriller	3
1961	Action	3
	Adventure	2
	Comedy	8
	Drama	7
	Fantasy	2
	Horror	3
	Music	1
	Romance	1
	Science Fiction	1
	Western	3
1962	Action	5
	Adventure	4
	Comedy	2
	Crime	3
	Drama	11
	Fantasy	1
	Horror	2
	Mystery	1
	Thriller	1
	Western	2
1963	Action	3
	Adventure	5
	Animation	1
	Comedy	9
	Drama	7
	Horror	6
	Mystery	2
	Romance	1
1964	Action	2
	Adventure	5
	Animation	2
	Comedy	10
	Crime	5

What kinds of properties are associated with movies that have high revenues?

# The movies with high revenues 

df_high = df[df['revenue_adj']>=df['revenue_adj'].mean()]

df_high.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2261 entries, 0 to 10848
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   popularity          2261 non-null   float64       
 1   original_title      2261 non-null   object        
 2   director            2261 non-null   object        
 3   runtime             2261 non-null   int64         
 4   release_date        2261 non-null   datetime64[ns]
 5   vote_count          2261 non-null   int64         
 6   vote_average        2261 non-null   float64       
 7   release_year        2261 non-null   int64         
 8   budget_adj          2261 non-null   float64       
 9   revenue_adj         2261 non-null   float64       
 10  profit              2261 non-null   float64       
 11  actor               2261 non-null   object        
 12  genre               2261 non-null   object        
 13  production_company  2261 non-null   object        
 14  release_month       2261 non-null   int64         
dtypes: datetime64[ns](1), float64(5), int64(4), object(5)
memory usage: 282.6+ KB

df_high['genre'].value_counts().plot(kind='bar', figsize=(12,5))

plt.title('Top 10 Most Grossed Genres', fontsize=16)
plt.xlabel('Genre', fontsize=14)
plt.ylabel('No of Movies', fontsize=14)

Text(0, 0.5, 'No of Movies')

df_high.describe()

	popularity	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	release_month
count	2261.000000	2261.00000	2261.000000	2261.000000	2261.000000	2.261000e+03	2.261000e+03	2.261000e+03	2261.000000
mean	1.557742	113.05617	772.407784	6.306369	1999.214065	5.830409e+07	2.282261e+08	1.699220e+08	7.232198
std	1.767902	24.06729	1051.490409	0.735536	12.460245	5.049916e+07	2.461158e+08	2.259461e+08	3.364514
min	0.010335	44.00000	10.000000	3.400000	1960.000000	0.000000e+00	5.152769e+07	-1.551102e+08	1.000000
25%	0.665367	98.00000	164.000000	5.800000	1992.000000	2.203826e+07	8.361457e+07	5.148183e+07	5.000000
50%	1.102836	110.00000	379.000000	6.300000	2002.000000	4.466253e+07	1.421194e+08	9.588923e+07	7.000000
75%	1.832434	124.00000	929.000000	6.800000	2009.000000	8.102293e+07	2.682159e+08	1.993909e+08	10.000000
max	32.985763	705.00000	9767.000000	8.300000	2015.000000	3.683713e+08	2.827124e+09	2.750137e+09	12.000000

# Most movies with high revenue have their vote_average above the general vote_average

df.vote_average.mean()

5.975011504832047

# Most movies with high revenue have their popularity_ratings above the general popularity average

df.popularity.mean()

0.6464455549010583

sns.pairplot(df_high)

<seaborn.axisgrid.PairGrid at 0x1e6f88bb310>

# Most genres in the high revenue movies 

df_high['genre'].value_counts().plot(kind='bar', figsize=(8,5))

plt.title('Top Genres in High Revenue Movies', fontsize=16)
plt.xlabel('Genres', fontsize=14)
plt.ylabel('No of Movies', fontsize=14)

Text(0, 0.5, 'No of Movies')

df_high['release_year'].value_counts().plot(kind='bar', figsize=(15,7))

plt.title('Years with the Most Number of High Revenue Movies', fontsize=16)
plt.xlabel('Year', fontsize=14)
plt.ylabel('No of Movies', fontsize=14)

Text(0, 0.5, 'No of Movies')

df_low = df[df['revenue_adj']<=df['revenue_adj'].mean()]

df_low.describe()

	popularity	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj	profit	release_month
count	8604.000000	8604.000000	8604.000000	8604.000000	8604.000000	8.604000e+03	8.604000e+03	8.604000e+03	8604.000000
mean	0.406971	99.185263	71.551953	5.887936	2001.875755	6.840312e+06	4.893656e+06	-1.946655e+06	6.721292
std	0.408321	32.426934	160.550302	0.962158	12.847880	1.628484e+07	1.119282e+07	1.555207e+07	3.454116
min	0.000065	0.000000	10.000000	1.500000	1960.000000	0.000000e+00	0.000000e+00	-4.139124e+08	1.000000
25%	0.175790	89.000000	15.000000	5.300000	1995.750000	0.000000e+00	0.000000e+00	-3.911485e+05	4.000000
50%	0.311120	96.000000	26.000000	5.900000	2006.000000	0.000000e+00	0.000000e+00	0.000000e+00	7.000000
75%	0.507358	108.000000	63.000000	6.600000	2012.000000	5.526546e+06	6.792659e+05	0.000000e+00	10.000000
max	8.411577	900.000000	5754.000000	9.200000	2015.000000	4.250000e+08	5.129772e+07	5.108839e+07	12.000000

# Most genres in the low revenue movies 

df_low['genre'].value_counts().plot(kind='bar', figsize=(8,5))

plt.title('Top Genres in Low Revenue Movies', fontsize=16)
plt.xlabel('Genres', fontsize=14)
plt.ylabel('No of Movies', fontsize=14)

Text(0, 0.5, 'No of Movies')

Conclusions:

Actors, directors and production companies that do more movies above the popularity and rating average are more profitable. Popularity of movies increase over the years. Profit and revenue increases per year mainly because of the increased number of movies released per year. The average quality of movies released declined over the years. The average budget for movies have reduced relatively over the years. That could be related to the fact that more low budget movies were released as the years become more recent.

Most movies with high revenues have vote averages and popularity ratings above the general averages. Comedy, Action and Drama seems to be the most common genres and also the most profitable